Abstract
In current Proteomics, prediction of protein-protein interactions (PPI) is a crucial aim as these interactions take part in most essential biological processes. In this paper, we propose a new approach to PPI dataset processing based on the extraction information from well-known databases and the application of data mining techniques. This approach will provide very accurate Support Vector Machine models, trained using high-confidence positive and negative examples. Finally, our proposed model has been validated using experimental, computational and literature-collected datasets.
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Urquiza, J.M., Rojas, I., Pomares, H., Herrera, L.J., Florido, J.P., Ortuño, F. (2011). Using Machine Learning Techniques and Genomic/Proteomic Information from Known Databases for PPI Prediction. In: Rocha, M.P., Rodríguez, J.M.C., Fdez-Riverola, F., Valencia, A. (eds) 5th International Conference on Practical Applications of Computational Biology & Bioinformatics (PACBB 2011). Advances in Intelligent and Soft Computing, vol 93. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-19914-1_48
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DOI: https://doi.org/10.1007/978-3-642-19914-1_48
Publisher Name: Springer, Berlin, Heidelberg
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